Neutrophil to lymphocyte ratio and in-hospital mortality among patients with SARS-CoV-2: A retrospective study.

The goal of this study was to investigate in-hospital mortality in patients suffering from acute respiratory syndrome coronavirus 2 (SARS-CoV-2) relative to the neutrophil to lymphocyte ratio (NLR) and to determine if there are gender disparities in outcome. Between February 26 and September 8, 2020, patients having SARS-CoV-2 infection were enrolled in this retrospective cohort research, which was categorized by NLR levels ≥9 and < 9. In total, 6893 patients were involved included of whom6591 had NLR <9, and 302 had NLR ≥9. The age of most of the patients in the NLR<9 group was 50 years, on the other hand, the age of most of the NLR ≥9 group patients was between 50 and 70 years. The majority of patients in both groups were male 2211 (66.1%). The ICU admission time and mortality rate for the patients with NLR ≥9 was significantly higher compared to patients with NLR <9. Logistic regression's outcome indicated that NLR ≥9 (odds ratio (OR), 24.9; 95% confidence interval (CI): 15.5-40.0; p < 0.001), male sex (OR, 3.5; 95% CI: 2.0-5.9; p < 0.001) and haemoglobin (HB) (OR, 0.95; 95% CI; 0.94-0.96; p < 0.001) predicted in-hospital mortality significantly. Additionally, Cox proportional hazards analysis (B = 4.04, SE = 0.18, HR = 56.89, p < 0.001) and Kaplan-Meier survival probability plots also indicated that NLR>9 had a significant effect on mortality. NLR ≥9 is an independent predictor of mortality(in-hospital) among SARS-CoV-2 patients.

In-hospital Mortality Rates in SARS-CoV-2 Patients Treated with Enoxaparin and Heparin.

OBJECTIVES: This study aimed to investigate in-hospital mortality rates in patients with coronavirus disease (COVID-19) according to enoxaparin and heparin use. METHODS: This retrospective cohort study included 962 patients admitted to two hospitals in Kuwait with a confirmed diagnosis of COVID-19. Cumulative all-cause mortality rate was the primary outcome. RESULTS: A total of 302 patients (males, 196 [64.9%]; mean age, 57.2 ± 14.6 years; mean body mass index, 29.8 ± 6.5 kg/m2) received anticoagulation therapy. Patients receiving anticoagulation treatment tended to have pneumonia (n = 275 [91.1%]) or acute respiratory distress syndrome (n = 106 [35.1%]), and high D-dimer levels (median [interquartile range]: 608 [523;707] ng/mL). The mortality rate in this group was high (n = 63 [20.9%]). Multivariable logistic regression, the Cox proportional hazards, and Kaplan-Meier models revealed that the use of therapeutic anticoagulation agents affected the risk of all-cause cumulative mortality. CONCLUSION: Age, hypertension, pneumonia, therapeutic anticoagulation, and methylprednisolone use were found to be strong predictors of in-hospital mortality. In elderly hypertensive COVID-19 patients on therapeutic anticoagulation were found to have 2.3 times higher risk of in-hospital mortality. All cause in-hospital mortality rate in the therapeutic anticoagulation group was up to 21%.

Correction to: Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.

[This corrects the article DOI: 10.1007/s42081-022-00165-z.].

In-hospital mortality in SARS-CoV-2 stratified by the use of corticosteroid.

Objective: To investigate COVID-19 related mоrtаlity according to the use of corticosteroid therapy. Design: Retrospective cohort study. Setting: Two tertiary hospitals in Kuwait. Participants: Overall, 962 patients with confirmed SARS-CoV-2 infection, were stratified according to whether they were treated with corticosteroids (dexamethasone or methylprednisolone). The mean age of the patients was 50.2 ± 15.9 years and 344/962 (35.9%) were female. Main outcome measures: In-hospital mortality and cumulative all-cause mortality. Results: Compared to non-corticosteroid therapy patients, corticosteroid therapy patients had a higher prevalence of hypertension, diabetes mellitus, cardiovascular disease, chronic lung disease, and chronic kidney disease; a longer hospital stay (median [IQR]: 17.0 [5.0-57.3] days vs 14.0 [2.0-50.2] days); and a higher in-hospital mortality (51/199 [25.6%] vs 36/763 [4.7%]). Logistic regression analysis showed a higher in-hospital mortality in the corticosteroid group (adjusted odds ratio [aOR]: 4.57, 95% confidence interval [CI]: 2.64-8.02, p < 0.001). Cox proportional hazards regression showed that corticosteroid use was a significant predictor of mortality (hazard ratio [HR]: 3.96, p < 0.001). Conclusions: In-hospital mortality in patients with SARS-CoV-2 on corticosteroid therapy was 4.6 times higher than in those without corticosteroid therapy.

Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.

In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait; stationarity test, cointegration test, Granger causality and stability test, and test on multivariate time-series using the Vector Error Correction Model (VECM) technique. The findings reveal that the concentration rate of air pollutants ( O 3 , SO 2 , NO 2 , CO , and PM 10 ) has an effect on COVID-19 admitted cases via Granger-cause. The Granger causation test shows that the concentration rate of air pollutants ( O 3 , PM 10 , NO 2 , temperature and wind speed) influences and predicts the COVID-19 admitted cases. The findings suggest that sulfur dioxide ( SO 2 ), NO 2 , temperature, and wind speed induce an increase in COVID-19 admitted cases in the short term according to VECM analysis. The evidence of a positive long-run association between COVID-19 admitted cases and environmental air pollution might be shown in the cointegration test and the VECM. There is an affirmation that the usage of air pollutants ( O 3 , SO 2 , NO 2 , CO , and PM 10 ) has a significant impact on COVID-19-admitted cases' prediction and its explained about 24% of increasing COVID-19 admitted cases in Kuwait.

In-hospital mortality in SARS-CoV-2 stratified by gamma-glutamyl transferase levels.

BACKGROUND: This study investigates in-hospital mortality amongst patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its relation to serum levels of gamma-glutamyl transferase (GGT). METHODS: Patients were stratified according to serum levels of gamma-glutamyl transferase (GGT) (GGT<50 IU/L or GGT≥50 IU/L). RESULTS: A total of 802 participants were considered, amongst whom 486 had GGT<50 IU/L and a mean age of 48.1 (16.5) years, whilst 316 had GGT≥50 IU/L and a mean age of 53.8 (14.7) years. The chief sources of SARS-CoV-2 transmission were contact (366, 45.7%) and community (320, 40%). Most patients with GGT≥50 IU/L had either pneumonia (247, 78.2%) or acute respiratory distress syndrome (ARDS) (85, 26.9%), whilst those with GGT<50 IU/L had hypertension (141, 29%) or diabetes mellitus (DM) (147, 30.2%). Mortality was higher amongst patients with GGT≥50 IU/L (54, 17.1%) than amongst those with GGT<50 IU/L (29, 5.9%). More patients with GGT≥50 required high (83, 27.6%) or low (104, 34.6%) levels of oxygen, whereas most of those with GGT<50 had no requirement of oxygen (306, 71.2%). Multivariable logistic regression analysis indicated that GGT≥50 IU/L (odds ratio [OR]: 2.02, 95% confidence interval [CI]: 1.20-3.45, p=0.009), age (OR: 1.05, 95% CI: 1.03-1.07, p<0.001), hypertension (OR: 2.06, 95% CI: 1.19-3.63, p=0.011), methylprednisolone (OR: 2.96, 95% CI: 1.74-5.01, p<0.001) and fever (OR: 2.03, 95% CI: 1.15-3.68, p=0.016) were significant predictors of all-cause cumulative mortality. A Cox proportional hazards regression model (B = -0.68, SE =0.24, HR =0.51, p = 0.004) showed that patients with GGT<50 IU/L had a 0.51-times lower risk of all-cause cumulative mortality than patients with GGT≥50 IU/L. CONCLUSION: Higher levels of serum GGT were found to be an independent predictor of in-hospital mortality.

Risk Factors for Mortality in Patients with COVID-19: The Kuwait Experience.

OBJECTIVES: To describe the baseline characteristics and to evaluate the risk factors for in-hospital mortality in patients admitted to hospitals with coronavirus disease (COVID-19) in Kuwait. SUBJECTS AND METHODS: This retrospective cohort study analyzed data of patients admitted to two hospitals in Kuwait with COVID-19. The outcome was assessed by using multivariable analysis of factors affecting survival and mortality. RESULTS: In 962 patients, the case fatality ratio was 9.04%. The mean age of nonsurvivors was 63.5 ± 14.8 years, and most deaths occurred in males (80.5%). For the whole sample, the source of transmission was significantly related to mortality and the median duration of in-hospital stay was 15 days (interquartile range: 2-52 days). In patients with high oxygen requirements, the case fatality rate was 96.6%. Multivariable analysis identified age, hypertension, cardiovascular disease (CVD), and dyspnea on presentation as independent risk factors for COVID-19 mortality. CONCLUSIONS: The mortality rate was higher in older patients with comorbidities such as hypertension and CVD. Early recognition of high-risk patients may help to improve care and reduce mortality.

Handling missing data in a rheumatoid arthritis registry using random forest approach.

Missing data in clinical epidemiological research violate the intention-to-treat principle, reduce the power of statistical analysis, and can introduce bias if the cause of missing data is related to a patient's response to treatment. Multiple imputation provides a solution to predict the values of missing data. The main objective of this study is to estimate and impute missing values in patient records. The data from the Kuwait Registry for Rheumatic Diseases was used to deal with missing values among patient records. A number of methods were implemented to deal with missing data; however, choosing the best imputation method was judged by the lowest root mean square error (RMSE). Among 1735 rheumatoid arthritis patients, we found missing values vary from 5% to 65.5% of the total observations. The results show that sequential random forest method can estimate these missing values with a high level of accuracy. The RMSE varied between 2.5 and 5.0. missForest had the lowest imputation error for both continuous and categorical variables under each missing data rate (10%, 20%, and 30%) and had the smallest prediction error difference when the models used the imputed laboratory values.

Ferritin level: A predictor of severity and mortality in hospitalized COVID-19 patients.

INTRODUCTION: This study aims to investigate in-hоsрitаl mоrtаlity in severe асute resрirаtоry syndrоme соrоnаvirus 2 Ñ€Ð°tients strаtified by serum ferritin levels. METHODS: Patients were stratified based on ferritin levels (ferritin levels ≤ 1000 or >1000). RESULTS: Approximately 89% (118) of the patients with ferritin levels > 1000 had pneumonia, and 51% (67) had hypertension. Fever (97, 73.5%) and shortness of breath (80, 61%) were two major symptoms among the patients in this group. Logistic regression analysis indicated that ferritin level (odds ratio [OR] = 0.36, 95% confidence interval [CI] = 0.21-0.62; p < .001), male sex (OR = 2.63, 95% CI = 1.43-5.06; p = .003), hypertension (OR = 4.16, 95% CI = 2.42-7.36; p < .001) and pneumonia (OR = 8.48, 95% CI = 3.02-35.45; p < .001) had significance in predicting in-hospital mortality. Additionally, the Cox proportional hazards analysis and Kaplan-Meier survival probability plot showed a higher mortality rate among patients with ferritin levels > 1000. CONCLUSION: In this study, higher levels of serum ferritin were found to be an independent predictor of in-hоsрitаl mоrtаlity.

In-hospital mortality in SARS-CoV-2 stratified by hemoglobin levels: A retrospective study.

This study is to estimate in-hospital mortality in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients stratified by hemoglobin (Hb) level. Patients were stratified according to hemoglobin level into two groups, that is, Hb <100 g/L and Hb >100 g/L. A total of 6931 patients were included. Of these, 6377 (92%) patients had hemoglobin levels >100 g/L. The mean age was 44 ± 17 years, and 66% of the patients were males. The median length of overall hospital stay was 13 days [2; 31]. The remaining 554 (8%) patients had a hemoglobin level <100 g/L. Overall mortality was 176 patients (2.54%) but was significantly higher in the group with hemoglobin levels <100 g/L (124, 22.4%) than in the group with hemoglobin levels >100 g/L (52, 0.82%). Risk factors associated with increased mortality were determined by multivariate analysis. The Kaplan-Meier survival analysis showed hemoglobin as a predictor of mortality. Cox proportional hazards regression coefficients for hemoglobin for the HB ≤ 100 category of hemoglobin were significant, B = 2.79, SE = 0.17, and HR = 16.34, p < 0.001. Multivariate logistic regression showed Hb < 100 g/L had a higher cumulative all-cause in-hospital mortality (22.4% vs. 0.8%; adjusted odds ratio [aOR], 0.33; 95% [CI]: [0.20-0.55]; p < 0.001). In this study, hemoglobin levels <100 g/L were found to be an independent predictor of in-hospital mortality.

In-hospital mortality in SARS-CoV-2 stratified by serum 25-hydroxy-vitamin D levels: A retrospective study.

This study is done to estimаte in-hоsрitаl mоrtаlity in раtients with severe асute resрirаtоry syndrоme соrоnаvirus 2 (SАRS-СоV-2) strаtified by Vitamin-D (Vit-D) levels. Раtients were strаtified ассоrding tо by serum 25-hydroxy-vitamin D (25(OH)Vit-D) levels intо twо grоuрs, that is, 25(OH)Vit-D less thаn 40 nmol/L аnd 25(OH)Vit-D greаter thаn 40 nmol/L. А tоtаl оf 231 раtients were inсluded. Оf these, 120 (50.2%) оf the раtients hаd 25(OH)Vit-D levels greаter thаn 40 nmol/L. The meаn аge wаs 49 ± 17 yeаrs, аnd 67% оf the раtients were mаles. The mediаn length оf оverаll hоsрitаl stаy wаs 18 [6; 53] dаys. The remаining 119 (49.8%) раtients hаd а 25(OH)Vit-D less thаn 40 nmol/L. Vitamin D levels were seen as deficient in 63% of patients, insufficient in 25% and normal in 12%. Оverаll mоrtаlity wаs 17 раtients (7.1%) but statistically not signifiсаnt among the grоuрs (p = 0.986). The Kарlаn-Meier survivаl аnаlysis shоwed no significance based on an alpha of 0.05, LL = 0.36, df = 1, p = 0.548, indicating Vitamin_D_Levels was not able to adequately predict the hazard of Mortality. In this study, serum 25(OH)Vit-D levels were found have no significance in terms of predicting the in-hоsрitаl mortality in раtients with SАRS-СоV-2.

Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018).

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

Influence of Ambient Air Pollution on Rheumatoid Arthritis Disease Activity Score Index.

Rheumatoid arthritis (RA) is a chronic autoimmune of an unknown etiology. Air pollution has been proposed as one of the possible risk factors associated with disease activity, although has not been extensively studied. In this study, we measured the relationship between exposure to air pollutants and RA activity. Data on RA patients were extracted from the Kuwait Registry for Rheumatic Diseases (KRRD). Disease activity was measured using disease activity score with 28 examined joints (DAS-28) and the Clinical Disease Activity Index (CDAI) during their hospital visits from 2013 to 2017. Air pollution was assessed using air pollution components (PM 10 , NO 2 , SO 2 , O 3 , and CO). Air pollution data were obtained from Kuwait Environmental Public Authority (K-EPA) from six different air quality-monitoring stations during the same period. Multiple imputations by the chained equations (MICE) algorithm were applied to estimate missing air pollution data. Patients data were linked with air pollution data according to date and patient governorate address. Descriptive statistics, correlation analysis, and linear regression techniques were employed using STATA software. In total, 1651 RA patients with 9875 follow-up visits were studied. We detected an increased risk of RA using DAS-28 in participants exposed to SO 2 and NO 2 with ß = 0 . 003 (95% CI: 0.0004-0.005, p < 0 . 01 ) and ß = 0 . 003 (95% CI: 0.002-0.005, p < 0 . 01 ), respectively, but not to PM 10 , O 3 , and CO concentrations. Conclusively, we observed a strong association between air pollution with RA disease activity. This study suggests air pollution as a risk factor for RA and recommends further measures to be taken by the authorities to control this health problem.

Estimation and tests for power-transformed and threshold GARCH models.

Consider a class of power-transformed and threshold GARCH ( p , q ) (PTTGRACH ( p , q ) ) model, which is a natural generalization of power-transformed and threshold GARCH(1,1) model in Hwang and Basawa [2004. Stationarity and moment structure for Box-Cox transformed threshold GARCH(1,1) processes. Statistics & Probability Letters 68, 209-220.] and includes the standard GARCH model and many other models as special cases. We first establish the asymptotic normality for quasi-maximum likelihood estimators (QMLE) of the parameters under the condition that the error distribution has finite fourth moment. For the case of heavy-tailed errors, we propose a least absolute deviations estimation (LADE) for PTTGARCH ( p , q ) model, and prove that the LADE is asymptotically normally distributed under very weak moment conditions. This paves the way for a statistical inference based on asymptotic normality for heavy-tailed PTTGARCH ( p , q ) models. As a consequence, we can construct the Wald test for GARCH structure and discuss the order selection problem in heavy-tailed cases. Numerical results show that LADE is more accurate than QMLE for heavy-tailed errors. Furthermore, the theory is applied to the daily returns of the Hong Kong Hang Seng Index, which suggests that asymmetry and nonlinearity could be present in the financial time series and the PTTGARCH model is capable of capturing these characteristics. As for the probabilistic structure of PTTGARCH ( p , q ) model, we give in the appendix a necessary and sufficient condition for the existence of a strictly stationary solution of the model, the existence of the moments and the tail behavior of the strictly stationary solution.